Rare earth (RE)–free permanent magnets, as alternative substitutes for RE-containing magnets for sustainable energy technologies and modern electronics, have attracted considerable interest. We performed a comprehensive search for new hard magnetic materials in the ternary Fe-Co-Zr space by leveraging a scalable, machine learning–assisted materials discovery framework running on GPU-enabled exascale computing resources. This framework integrates a crystal graph convolutional neural network (CGCNN) machine learning (ML) method with first-principles calculations to efficiently navigate the vast composition-structure space. The efficiency and accuracy of the ML approach enable us to reveal 9 new thermodynamically stable ternary Fe-Co-Zr compounds and 81 promising low-energy metastable phases with their formation energies within 0.1 eV/atom above the convex hull. The predicted compounds span a wide range of crystal symmetries and magnetic behaviors, providing a rich platform for tuning functional properties. Based on the analysis of site-specific magnetic properties, we show that the ${\mathrm{Fe}}_6{\mathrm{Co}}_{17}{\mathrm{Zr}}_6$ compound obtained from our ML discovery can be further optimized by chemical doping. Chemical substitutions lead to a ternary ${\mathrm{Fe}}_5{\mathrm{Co}}_{18}{\mathrm{Zr}}_6$ phase with a strong anisotropy of ${\mathrm{K}}_1=1.1\mathrm{MJ}\text{/}{\mathrm{m}}^3$ and a stable quaternary magnetic ${\mathrm{Fe}}_5{\mathrm{Co}}_{16}{\mathrm{Zr}}_6{\mathrm{Mn}}_4$ compound.

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• Magnetic anisotropy
• Permanent magnets
• Deep learning
• First-principles calculations
• Machine learning